PseudoFuN: Deriving functional potentials of pseudogenes from integrative relationships with genes and microRNAs across 32 cancers

BACKGROUND: Long thought "relics" of evolution, not until recently have pseudogenes been of medical interest regarding regulation in cancer. Often, these regulatory roles are a direct by-product of their close sequence homology to protein-coding genes. Novel pseudogene-gene (PGG) functional associations can be identified through the integration of biomedical data, such as sequence homology, functional pathways, gene expression, pseudogene expression, and microRNA expression. However, not all of the information has been integrated, and almost all previous pseudogene studies relied on 1:1 pseudogene-parent gene relationships without leveraging other homologous genes/pseudogenes. RESULTS: We produce PGG families that expand beyond the current 1:1 paradigm. First, we construct expansive PGG databases by (i) CUDAlign graphics processing unit (GPU) accelerated local alignment of all pseudogenes to gene families (totaling 1.6 billion individual local alignments and >40,000 GPU hours) and (ii) BLAST-based assignment of pseudogenes to gene families. Second, we create an open-source web application (PseudoFuN [Pseudogene Functional Networks]) to search for integrative functional relationships of sequence homology, microRNA expression, gene expression, pseudogene expression, and gene ontology. We produce four "flavors" of CUDAlign-based databases (>462,000,000 PGG pairwise alignments and 133,770 PGG families) that can be queried and downloaded using PseudoFuN. These databases are consistent with previous 1:1 PGG annotation and also are much more powerful including millions of de novo PGG associations. For example, we find multiple known (e.g., miR-20a-PTEN-PTENP1) and novel (e.g., miR-375-SOX15-PPP4R1L) microRNA-gene-pseudogene associations in prostate cancer. PseudoFuN provides a "one stop shop" for identifying and visualizing thousands of potential regulatory relationships related to pseudogenes in The Cancer Genome Atlas cancers. CONCLUSIONS: Thousands of new PGG associations can be explored in the context of microRNA-gene-pseudogene co-expression and differential expression with a simple-to-use online tool by bioinformaticians and oncologists alike

Pseudogene-gene functional networks are prognostic of patient survival in breast cancer

Background: Given the vast range of molecular mechanisms giving rise to breast cancer, it is unlikely universal cures exist. However, by providing a more precise prognosis for breast cancer patients through integrative models, treatments can become more individualized, resulting in more successful outcomes. Specifically, we combine gene expression, pseudogene expression, miRNA expression, clinical factors, and pseudogene-gene functional networks to generate these models for breast cancer prognostics. Establishing a LASSO-generated molecular gene signature revealed that the increased expression of genes STXBP5, GALP and LOC387646 indicate a poor prognosis for a breast cancer patient. We also found that increased CTSLP8 and RPS10P20 and decreased HLA-K pseudogene expression indicate poor prognosis for a patient. Perhaps most importantly we identified a pseudogene-gene interaction, GPS2-GPS2P1 (improved prognosis) that is prognostic where neither the gene nor pseudogene alone is prognostic of survival. Besides, miR-3923 was predicted to target GPS2 using miRanda, PicTar, and TargetScan, which imply modules of gene-pseudogene-miRNAs that are potentially functionally related to patient survival. Results: In our LASSO-based model, we take into account features including pseudogenes, genes and candidate pseudogene-gene interactions. Key biomarkers were identified from the features. The identification of key biomarkers in combination with significant clinical factors (such as stage and radiation therapy status) should be considered as well, enabling a specific prognostic prediction and future treatment plan for an individual patient. Here we used our PseudoFuN web application to identify the candidate pseudogene-gene interactions as candidate features in our integrative models. We further identified potential miRNAs targeting those features in our models using PseudoFuN as well. From this study, we present an interpretable survival model based on LASSO and decision trees, we also provide a novel feature set which includes pseudogene-gene interaction terms that have been ignored by previous prognostic models. We find that some interaction terms for pseudogenes and genes are significantly prognostic of survival. These interactions are cross-over interactions, where the impact of the gene expression on survival changes with pseudogene expression and vice versa. These may imply more complicated regulation mechanisms than previously understood. Conclusions: We recommend these novel feature sets be considered when training other types of prognostic models as well, which may provide more comprehensive insights into personalized treatment decisions

Network analysis of pseudogene-gene relationships: from pseudogene evolution to their functional potentials

Pseudogenes are fossil relatives of genes. Pseudogenes have long been thought of as "junk DNAs", since they do not code proteins in normal tissues. Although most of the human pseudogenes do not have noticeable functions, ∼20% of them exhibit transcriptional activity. There has been evidence showing that some pseudogenes adopted functions as lncRNAs and work as regulators of gene expression. Furthermore, pseudogenes can even be "reactivated" in some conditions, such as cancer initiation. Some pseudogenes are transcribed in specific cancer types, and some are even translated into proteins as observed in several cancer cell lines. All the above have shown that pseudogenes could have functional roles or potentials in the genome. Evaluating the relationships between pseudogenes and their gene counterparts could help us reveal the evolutionary path of pseudogenes and associate pseudogenes with functional potentials. It also provides an insight into the regulatory networks involving pseudogenes with transcriptional and even translational activities.In this study, we develop a novel approach integrating graph analysis, sequence alignment and functional analysis to evaluate pseudogene-gene relationships, and apply it to human gene homologs and pseudogenes. We generated a comprehensive set of 445 pseudogene-gene (PGG) families from the original 3,281 gene families (13.56%). Of these 438 (98.4% PGG, 13.3% total) were non-trivial (containing more than one pseudogene). Each PGG family contains multiple genes and pseudogenes with high sequence similarity. For each family, we generate a sequence alignment network and phylogenetic trees recapitulating the evolutionary paths. We find evidence supporting the evolution history of olfactory family (both genes and pseudogenes) in human, which also supports the validity of our analysis method. Next, we evaluate these networks in respect to the gene ontology from which we identify functions enriched in these pseudogene-gene families and infer functional impact of pseudogenes involved in the networks. This demonstrates the application of our PGG network database in the study of pseudogene function in disease context

Determination of specific heat capacity on composite shape-stabilized phase change materials and asphalt mixtures by heat exchange system

Previous research has shown that composite shape-stabilized phase change material (CPCM) has a remarkable capacity for thermal storage and stabilization, and it can be directly applied to highway construction without leakage. However, recent studies on temperature changing behaviors of CPCM and asphalt mixture cannot intuitively reflect the thermoregulation mechanism and efficiency of CPCM on asphalt mixture. The objective of this paper is to determine the specific heat capacity of CPCM and asphalt mixtures mixed with CPCM using the heat exchange system and the data acquisition system. Studies have shown that the temperature-rise curve of 5 °C CPCM has an obvious temperature plateau, while an asphalt mixture mixed with 5 °C CPCM does not; with increasing temperature, the specific heat capacities of both 5 °C CPCM and asphalt mixture first increase and then decrease, while the variation rate of 5 °C CPCM is larger than that of the asphalt mixture, and the maximum specific heat capacity of 5 °C CPCM appears around the initial phase change temperature. It is concluded that the temperature intervals of 5 °C CPCM are −18 °C–7 °C, 7 °C–25 °C and 25 °C–44 °C, respectively, and that of the asphalt mixture are −18 °C~10 °C, −10 °C~5 °C and 5 °C~28 °C. A low dosage of 5 °C CPCM has little influence on the specific heat capacity of asphalt mixture. Finally, the functions of specific heat capacities and temperature for CPCM and asphalt mixture mixed with CPCM were recommended by the sectional regression method

AltNeRF: Learning Robust Neural Radiance Field via Alternating Depth-Pose Optimization

Neural Radiance Fields (NeRF) have shown promise in generating realistic novel views from sparse scene images. However, existing NeRF approaches often encounter challenges due to the lack of explicit 3D supervision and imprecise camera poses, resulting in suboptimal outcomes. To tackle these issues, we propose AltNeRF -- a novel framework designed to create resilient NeRF representations using self-supervised monocular depth estimation (SMDE) from monocular videos, without relying on known camera poses. SMDE in AltNeRF masterfully learns depth and pose priors to regulate NeRF training. The depth prior enriches NeRF's capacity for precise scene geometry depiction, while the pose prior provides a robust starting point for subsequent pose refinement. Moreover, we introduce an alternating algorithm that harmoniously melds NeRF outputs into SMDE through a consistence-driven mechanism, thus enhancing the integrity of depth priors. This alternation empowers AltNeRF to progressively refine NeRF representations, yielding the synthesis of realistic novel views. Additionally, we curate a distinctive dataset comprising indoor videos captured via mobile devices. Extensive experiments showcase the compelling capabilities of AltNeRF in generating high-fidelity and robust novel views that closely resemble reality

Efficient Generalization Improvement Guided by Random Weight Perturbation

To fully uncover the great potential of deep neural networks (DNNs), various learning algorithms have been developed to improve the model's generalization ability. Recently, sharpness-aware minimization (SAM) establishes a generic scheme for generalization improvements by minimizing the sharpness measure within a small neighborhood and achieves state-of-the-art performance. However, SAM requires two consecutive gradient evaluations for solving the min-max problem and inevitably doubles the training time. In this paper, we resort to filter-wise random weight perturbations (RWP) to decouple the nested gradients in SAM. Different from the small adversarial perturbations in SAM, RWP is softer and allows a much larger magnitude of perturbations. Specifically, we jointly optimize the loss function with random perturbations and the original loss function: the former guides the network towards a wider flat region while the latter helps recover the necessary local information. These two loss terms are complementary to each other and mutually independent. Hence, the corresponding gradients can be efficiently computed in parallel, enabling nearly the same training speed as regular training. As a result, we achieve very competitive performance on CIFAR and remarkably better performance on ImageNet (e.g. $\mathbf{ +1.1\%}$) compared with SAM, but always require half of the training time. The code is released at https://github.com/nblt/RWP

3-[(R)-3,3-Dichloro-2-hydroxy­prop­yl]-8-hydr­oxy-6-meth­oxy-1H-isochromen-1-one

The title compound, C13H12Cl2O5, is an isocoumarin compound which has been isolated from the ethyl acetate extract of the fermentation broth of actinomycete Streptomyces sp. (V4) from the South China Sea. There are intra- and inter­molecular hydrogen bonds and halogen bonds [Cl⋯Cl = 3.434 (2) Å; C—Cl⋯Cl = 121.6°]. The intermolecular O—H⋯O hydrogen bonds link mol­ecules into chains along the b axis

(3E,5E)-3,5-Bis(4-hy­droxy-3,5-di­methoxy­benzyl­idene)oxan-4-one monohydrate

In the title compound, C23H24O8·H2O, the six-membered ring of the oxan-4-one (tetra­hydro­pyran-4-one) ring displays an envelope conformation with the heterocyclic O atom at the flap position. The dihedral angles between the terminal benzene rings is 37.23 (10)°. Classical intermolecular O—H⋯O and weak C—H⋯O hydrogen bonds are present in the crystal structure

Swimming exercise ameliorates hypertension-induced kidney dysfunction via alleviating renal interstitial fibrosis and apoptosis

Background: Hypertensive nephropathy is one of the major causes of ESRD. Exercise has been considered a nonpathological therapy for hypertension and its complications, yet mechanisms remain unclear. We sought to investigate whether periodic swimming could ameliorate hypertension-induced kidney dysfunction and its underlying mechanisms. Methods: Four-week male spontaneously hypertensive rats (SHRs) were randomly divided into the hypertension group (SHR, n = 8) and exercise group (SE, n = 8, 60 min swimming/day, 6 days per week, for 8 weeks). Wistar-Kyoto rats (WKY, n = 8) were served as a sedentary normotensive group. Bodyweight and blood pressure (BP) were recorded weekly. After 8-week sedentary or swimming exercise, lipids profile, BUN, and Cr were measured. The renal interstitial fibrosis was examined by the histopathological analysis using Masson\u27s trichrome staining and hematoxylin and eosin staining. The kidney cell apoptosis was tested by TUNEL staining. The expressions of critical proteins responsible for the TGF-β1/Smad signaling of fibrosis, that is, TGF-β1, Smad2/3, and Smad7, as well as apoptosis related proteins, Bax and Bcl-2 in kidney cortex tissues were measured. Results: The 8-week swimming exercise reduced BP and bodyweight, lowered concentrations of BUN, and serum Cr, compared with SHR. Exercise remarkably inhibited hypertension-induced tubular degeneration, cellular cluster, and tubular cell swelling as well as glomerular degeneration in the kidney cortical tissues, attenuated renal interstitial fibrosis, and renal cell apoptosis. Moreover, expressions of TGF-β1, Smad2/3, and Bax were higher in the SHR than the WKY, which were significantly suppressed by the exercise. In contrast, hypertension-reduced expressions of Smad7 and Bcl-2 were enhanced by the swimming exercise. Strong correlations were found between kidney function indices, blood lipids, and key protein expressions. Conclusion: Our results demonstrate beneficial effects of the periodic swimming on ameliorating hypertension-induced kidney dysfunction highlighting the potential of swimming exercise as a nonpathological therapy for early prevention of hypertension-caused kidney diseases